Evidence map›Paper›PMID 40204727›Full record

ArticleNature communications2025

Development and validation of prediction algorithm to identify tuberculosis in two large California health systems.

Heidi Fischer, Lei Qian, Zhuoxin Li, Katia Bruxvoort, Jacek Skarbinski, Yuching Ni, Jennifer H Ku, Bruno Lewin, Saadiq Garba, Parag Mahale and 3 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Heidi FischerDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA. Heidi.Fischer@kp.org.ORCID http://orcid.org/0000-0001-5343-0002
Lei QianDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Zhuoxin LiDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Katia BruxvoortDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.ORCID http://orcid.org/0000-0001-8277-2431
Jacek SkarbinskiDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Yuching NiDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Jennifer H KuDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Bruno LewinDepartment of Family Medicine, Kaiser Permanente Los Angeles Medical Center, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-1483-2791
Saadiq GarbaDepartment of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA.
Parag MahaleDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Sally F ShawDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-6553-1772
Brigitte SpenceDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Sara Y TartofDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.ORCID http://orcid.org/0000-0003-2336-8476

Funding

A New Approach to an Old Problem: Redesigning Latent Tuberculosis Screening and TreatmentR01AI151072 · NIAID · KAISER FOUNDATION RESEARCH INSTITUTE · PI TARTOF, SARA · 2020 to 2024
$3.9M
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) 5R01AI151072NIAID NIH HHS R01 AI151072
6 · The paper itself

Abstract

California data demonstrate failures in latent tuberculosis screening to prevent progression to tuberculosis disease. Therefore, we developed a clinical risk prediction model for tuberculosis disease using electronic health records. This study included Kaiser Permanente Southern California and Northern California members ≥18 years during 2008-2019. Models used Cox proportional hazards regression, Harrell's C-statistic, and a simulated TB disease outcome accounting for cases prevented by current screening which includes both observed and simulated cases. We compared sensitivity and number-needed-to-screen for model-identified high-risk individuals with current screening. Of 4,032,619 and 4,051,873 Southern and Northern California members, tuberculosis disease incidences were 4.1 and 3.3 cases per 100,000 person-years, respectively. The final model C-statistic was 0.816 (95% simulation interval 0.805-0.824). Model sensitivity screening high-risk individuals was 0.70 (0.68-0.71) and number-needed-to-screen was 662 (646-679) persons-per tuberculosis disease case, compared to a sensitivity of 0.36 (0.34-0.38) and number-needed-to-screen of 1632 (1485-1774) with current screening. Here, we show our predictive model improves tuberculosis screening efficiency in California.

Indexed as

Prediction AlgorithmsRegional Health PlanningTuberculosisAdolescentAdultAgedCaliforniaDatasets as TopicElectronic Health RecordsFemaleHumansMaleMass ScreeningMiddle AgedRisk FactorsYoung Adult

Identifiers

PMID40204727
PMCPMC11982269

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.